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What you'll learn?
- How to effectively proprocess data before analysis.
- How to implement different preprocessing methods using matlab.
- Take away code templates for quickly preprocessing your data
Course Overview
This course is for you if you want to fully equip yourself with the art of applied machine learning using MATLAB. This course is also for you if you want to apply the most commonly used data preprocessing techniques without having to learn all the complicated maths. Additionally, this course is also for you if you have had previous hours and hours of machine learning implementation but could never figure out how to further improve the performance of the machine learning algorithms. By the end of this course, you will have at your fingertips, a vast variety of most commonly used data preprocessing techniques that you can use instantly to maximize your insight into your data set.
The approach in this course is very practical and we will start everything from very scratch. We will immediately start coding after a couple of introductory tutorials and we try to keep the theory to bare minimal. All the coding will be done in MATLAB which is one of the fundamental programming languages for engineer and science students and is frequently used by top data science research groups worldwide.
Pre-requisites
- MATLAB 2017a or heigher version. No prior knowledge of MATLAB is required
- In version below 2017a there might be some functions that will not work
- We cover everything from scratch and therefore do not require any prior knowledge of MATLAB
Target Audience
- Students, Entrepreneurs, Researchers, Instructors, Engineers, Programmers, Simulators
- Anyone who want to analyze the data
Curriculum 30 Lectures 04:14:03
-
Section 1 : Introduction to course and MATLAB
- Lecture 2 :
- Introduction to MATLAB
- Lecture 3 :
- Importing Dataset into MATLAB
-
Section 2 : Handling Missing Data
- Lecture 1 :
- Code and Data
- Lecture 2 :
- Deletion Strategies
- Lecture 3 :
- Using mean and mode
- Lecture 4 :
- Adding Special Value
- Lecture 5 :
- Class Specific Mode Mean
- Lecture 6 :
- Random Value Imputation
-
Section 3 : Dealing with Categorical data
- Lecture 1 :
- Code and Data
- Lecture 2 :
- Categorical data with no order
- Lecture 3 :
- Categorical data with order
- Lecture 4 :
- Frequency Encoding
- Lecture 5 :
- Target Based Encoding
-
Section 4 : Outliers
- Lecture 1 :
- Code and Data
- Lecture 2 :
- 3 sigma rule with deletion dtrategy
- Lecture 3 :
- 3 sigma rule with filling strategy
- Lecture 4 :
- Histograms for outliers
- Lecture 5 :
- Box Plots (Part 1)
- Lecture 6 :
- Box Plots (Part 2
- Lecture 7 :
- LOF (Part 1)
- Lecture 8 :
- LOF (Part 2)
- Lecture 9 :
- Outliers in categorical variables
-
Section 5 : Feature Scalling and Discretization
- Lecture 1 :
- Code and Data
- Lecture 2 :
- Feature Scalling
- Lecture 3 :
- Equal Width Binning
- Lecture 4 :
- Equal Frequency Binning
-
Section 6 : Project-Selecting Approperiate Methods
- Lecture 1 :
- Code and Data
- Lecture 2 :
- Selecting the right method (Part 1)
- Lecture 3 :
- Selecting the right method (Part 2)
Our learners work at
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Instructor
90594 Course Views
2 Courses
I am Dr. Nouman Azam and i am Assistant Professor in Computer Science. I teach online courses related to MATLAB Programming to more than 10,000 students on different online plateforms.
The focus in these courses is to explain different aspects of MATLAB and how to use them effectively in routine daily life activities. In my courses, you will find topics such as MATLAB programming, designing gui's, data analysis and visualization.
Machine learning techinques using MATLAB is one of my favourate topic. During my research career i explore the use of MATLAB in implementing machine learning techniques such as bioinformatics, text summarization, text categorization, email filtering, malware analysis, recommender systems and medical decision making.
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